An Introduction to Artificial Immune Systems

Dr. Jonathan Timmis

Computing Laboratory

University of Kent at Canterbury

CT2 7NF. UK.

J.Timmis@ukc.ac.uk

http:/www.cs.ukc.ac.uk/people/staff/jt6

ES2001Cambridge. December 2001.

Overview of TutorialWhat are we going to do?:First Half:

Describe what is an AISWhy bother with the immune system?Be familiar with relevant immunology

Second Half:Appreciation of were AIS are usedBe familiar with the building blocks of AIS

Resources

Immune metaphors

Immune System

Idea! Idea ‘

Other areas

Artificial Immune Systems

Why the Immune System?Recognition

Anomaly detectionNoise tolerance

RobustnessFeature extractionDiversityReinforcement learningMemoryDistributedMulti-layeredAdaptive

Artificial Immune Systems

AIS are computational systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains (de Castro & Timmis, 2001)

Some History

Developed from the field of theoretical immunology in the mid 1980’s.

Suggested we ‘might look’ at the IS

1990 – Bersini first use of immune algos to solve problemsForrest et al – Computer Security mid 1990’sHunt et al, mid 1990’s – Machine learning

Scope of AISFault and anomaly detectionData Mining (machine learning, Pattern recognition)Agent based systemsSchedulingAutonomous controlOptimisationRoboticsSecurity of information systems

Part I – Basic Immunology

Role of the Immune System

Protect our bodies from infection

Primary immune responseLaunch a response to invading pathogens

Secondary immune responseRemember past encounters

Faster response the second time around

How does it work?

Where is it?

Multiple layers of the immune system

Phagocyte

Adaptive immune

response

Lymphocytes

Innate immune

response

Biochemical barriers

Skin

Pathogens

Immune Pattern Recognition

The immune recognition is based on the complementarity between the binding region of the receptor and a portion of the antigen called epitope.

Antibodies present a single type of receptor, antigens might present several epitopes.

This means that different antibodies can recognize a single antigen

Antibodies

Antigen binding sites

VH

VL

CH CH

VL

CL

CH

VH

CH

CL

Fc

FabFab

Antibody Molecule Antibody Production

Clonal Selection

Main Properties of Clonal Selection (Burnet, 1978)

Elimination of self antigens

Proliferation and differentiation on contact of mature lymphocytes with antigen

Restriction of one pattern to one differentiated cell and retention of that pattern by clonal descendants;

Generation of new random genetic changes, subsequently expressed as diverse antibody patterns by a form of accelerated somatic mutation

T-cells

Regulation of other cells

Active in the immune responseHelper T-cells

Killer T-cells

Reinforcement Learning and Immune Memory

Repeated exposure to an antigen throughout a lifetime

Primary, secondary immune responses

Remembers encountersNo need to start from scratch

Memory cells

Associative memory

Learning (2)

Antigen Ag1 Antigens Ag1, Ag2

Primary Response Secondary Response

Lag

Response to Ag1

Ant

ibod

y C

once

ntra

tion

Time

Lag

Response to Ag2

Response to Ag1

...

...

Cross-Reactive Response

...

...

Antigen Ag1 + Ag3

Response to Ag1 + Ag3

Lag

Immune Network Theory

Idiotypic network (Jerne, 1974)

B cells co-stimulate each otherTreat each other a bit like antigens

Creates an immunological memory

Immune Network Theory(2)

Shape Space Formalism

Repertoire of the immune system is complete (Perelson, 1989)

Extensive regions of complementarity

Some threshold of recognition

V

V

V

V

Self/Non-Self Recognition

Immune system needs to be able to differentiate between self and non-self cells

Antigenic encounters may result in cell death, therefore

Some kind of positive selection

Some element of negative selection

Summary so far ….

Immune system has some remarkable properties

Pattern recognition

Learning

Memory

So, is it useful?

Some questions for you !

Part II –Artificial Immune Systems

This Section

General Framework for describing and constructing AIS

A short review of where AIS are used todayCan not cover them all, far too many

I am not an expert in all areas (earn more money if I was)

Where are AIS headed?

What do want from a Framework?

In a computational world we work with representations and processes. Therefore, we need:

To be able to describe immune system componentsBe able to describe their interactionsQuite high level abstractionsCapture general purpose processes that can be applied to various areas

AIS Framework

De Castro & Timmis, 2002

Immune Representations

Immune Algorithms

Guidelines for developing AIS

Representation – Shape Space

Describe the general shape of a molecule

•Describe interactions between molecules

•Degree of binding between molecules

•Complement threshold

Representation

Vectors

Ab = Ab1, Ab2, ..., AbL

Ag = Ag1, Ag2, ..., AgL

Real-valued shape-space

Integer shape-space

Hamming shape-space

Symbolic shape-space

Define their InteractionDefine the term AffinityAffinity is related to distance

Euclidian

L

iii AgAbD

1

2)(

• Other distance measures such as Hamming, Manhattan etc. etc.

• Affinity Threshold

Basic Immune Models and Algorithms

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection Algorithm

Somatic Hypermutation

Immune Network Models

Bone Marrow ModelsGene libraries are used to create antibodies from the bone marrowAntibody production through a random concatenation from gene librariesSimple or complex libraries

Negative Selection AlgorithmsForrest 1994: Idea taken from the negative selection of T-cells in the thymusApplied initially to computer securitySplit into two parts:

CensoringMonitoring

Negative Selection AlgorithmEach copy of the algorithm is unique, so that each protected location is provided with a unique set of detectorsDetection is probabilistic, as a consequence of using different sets of detectors to protect each entityA robust system should detect any foreign activity rather than looking for specific known patterns of intrusion. No prior knowledge of anomaly (non-self) is requiredThe size of the detector set does not necessarily increase with the number of strings being protectedThe detection probability increases exponentially with the number of independent detection algorithmsThere is an exponential cost to generate detectors with relation to the number of strings being protected (self).

Solution to the above in D’haeseleer et al. (1996)

Clonal Selection Algorithmde Castro & von Zuben, 2001

Randomly initialise a population (P)For each pattern in Ag

Determine affinity to each P’Select n highest affinity from P

Clone and mutate prop. to affinity with Ag

Add new mutants to P endForSelect highest affinity P to form part of MReplace n number of random new ones

Until stopping criteria

Immune Network Models

Timmis & Neal, 2000

Used immune network theory as a basis, proposed the AINE algorithmInitialize AINFor each antigen

Present antigen to each ARB in the AINCalculate ARB stimulation levelAllocate B cells to ARBs, based on stimulation levelRemove weakest ARBs (ones that do not hold any B cells)

If termination condition metexit

elseClone and mutate remaining ARBsIntegrate new ARBs into AIN

Immune Network ModelsDe Castro & Von Zuben (2000c)

aiNET, based in similar principlesAt each iteration step do

For each antigen doDetermine affinity to all network cellsSelect n highest affinity network cellsClone these n selected cells

Increase the affinity of the cells to antigen by reducing the distance between them (greedy search)

Calculate improved affinity of these n cellsRe-select a number of improved cells and place into matrix MRemove cells from M whose affinity is below a set thresholdCalculate cell-cell affinity within the networkRemove cells from network whose affinity is below

a certain thresholdConcatenate original network and M to form new network

Determine whole network inter-cell affinities and remove all those below the set threshold

Replace r% of worst individuals by novel randomly generated onesTest stopping criterion

Somatic HypermutationMutation rate in proportion to affinityVery controlled mutation in the natural immune systemTrade-off between the normalized antibody affinity D* and its mutation rate ,

Part III - Applications

Anomaly DetectionThe normal behavior of a system is often characterized by a series of observations over time. The problem of detecting novelties, or anomalies, can be viewed as finding deviations of a characteristic property in the system.For computer scientists, the identification of computational viruses and network intrusions is considered one of the most important anomaly detection tasks

Virus DetectionProtect the computer from unwanted virusesInitial work by Kephart 1994More of a computer immune system

Detect Anomaly

Scan for known viruses

Capture samples using decoys

Extract Signature(s)

Add signature(s) to databases

Add removal infoto database

Segregatecode/data

AlgorithmicVirus Analysis

Send signals toneighbor machines

Remove Virus

Virus Detection (2)Okamoto & Ishida (1999a,b) proposed a distributed approach Detected viruses by matching self-information

first few bytes of the head of a file the file size and path, etc. against the current host files.

Viruses were neutralized by overwriting the self-information on the infected filesRecovering was attained by copying the same file from other uninfected hosts through the computer network

Virus Detection (3)Other key works include:

A distributed self adaptive architecture for a computer virus immune system (Lamont, 200)Use a set of co-operating agents to detect non-self patterns

Immune System Computational System

Pathogens (antigens) Computer viruses

B-, T-cells and antibodies Detectors

Proteins Strings

Antibody/antigen binding Pattern matching

Security

Somayaji et al. (1997) outlined mappings between IS and computer systemsA security systems need

ConfidentialityIntegrityAvailabilityAccountability Correctness

IS to Security SystemsImmune System Network Environment

Static Data

Self Uncorrupted data

Non-self Any change to self

Active Processes on Single Host

Cell Active process in a computer

Multicellular organism Computer running multiple processes

Population of organisms Set of networked computers

Skin and innate immunity Security mechanisms, like passwords, groups, file permissions, etc.

Adaptive immunity Lymphocyte process able to query other processes to seek for abnormal behaviors

Autoimmune response False alarm

Self Normal behavior

Non-self Abnormal behavior

Network of Mutually Trusting Computers

Organ in an animal Each computer in a network environment

Network Security

Hofmeyr & Forrest (1999, 2000): developing an artificial immune system that is distributed, robust, dynamic, diverse and adaptive, with applications to computer network security.

Kim & Bentley (2001). Hybrid approach of clonal selection and negative selection.

Forrests Model

  AIS for computer network security. (a) Architecture. (b) Life cycle of a detector.

Datapath triple

(20.20.15.7, 31.14.22.87, ftp)

Broadcast LAN

ip: 31.14.22.87port: 2000

Internal host

External host

ip: 20.20.15.7 port: 22

Host

Activationthreshold

Cytokinelevel

Permutationmask

Detectorset

immature memory activated matches

0100111010101000110......101010010

Detector

Randomly created

Immature

Mature & Naive

Death

Activated

Memory

No match duringtolerization

010011100010.....001101

Exceed

activationthreshold

Don’t exceed

activation threshold

No co stimulation

Co stimulation

Match

Match during

tolerization

Novelty DetectionImage Segmentation : McCoy & Devarajan (1997)

Detecting road contours in aerial imagesUsed a negative selection algorithm

Hardware Fault Tolerance

Immunotronics (Bradley & Tyrell, 2000)

Use negative selection algorithm for fault tolerance in hardware

Table 4.1.           

Immune System Hardware Fault Tolerance

Recognition of self Recognition of valid state/state transition

Recognition of non-self Recognition of invalid state/state transition

Learning Learning correct states and transitions

Humoral immunity Error detection and recovery

Clonal deletion Isolation of self-recognizing tolerance conditions

Inactivation of antigen Return to normal operation

Life of an organism Operation lifetime of a hardware

 

Machine Learning

Early work on DNA Recognition Cooke and Hunt, 1995

Use immune network theory

Evolve a structure to use for prediction of DNA sequences

90% classification rate

Quite good at the time, but needed more corroboration of results

Unsupervised Learning

Timmis, 2000Based on Hunts work

Complete redesign of algorithm: AINE

Immune metadynamics

Shape space

Few initial parameters

Stabilises to find a core pattern within a network of B cells

Results (Timmis, 2000)

Immune System : AIS

B-cell

B-cell recognition

Immune Network

Somatic Hypermutation

Antigens

Antigen binding

Initial DataArtificial Recognition BallARB NetworkMutation of ARB’s

Training dataMatching between antigen and ARB’s

Another approach

de Castro and von Zuben, 2000aiNET cf. SOFMUse similar ideas to Timmis

• Immune network theory• Shape space

Suppression mechanism different• Eliminate self similar cells under a set threshold

Clone based on antigen match, network not taken into account

Results (de Castro & von Zuben, 2001)

Test Problem Result from aiNET

Supervised Approach

Carter, 2000Pattern recognition and classification system: Immunos-81

Use T-cells, B-cells, antibodies and amino-acid library

Builds a library of data types and classes

Watkins, 2001Resource allocated mechanism (based on network models)

Good classification rates on sample data sets

RoboticsBehaviour Arbitration

Ishiguro et al. (1996, 1997) : Immune network theory to evolve a behaviour among a set of agents

Collective BehaviourEmerging collective behaviour through communicating robots (Jun et al, 1999)Immune network theory to suppress or encourage robots behaviour

Desirable Interacting antibodiescondition and degree of interaction

Action

Paratope Idiotope

SchedulingHart et al. (1998) and Hart & Ross (1999a)Proposed an AIS to produce robust schedules

for a dynamic job-shop scheduling problem in which jobs arrive continually, and the environment is subject to changes.

Investigated is an AIS could be evolved using a GA approach

then be used to produce sets of schedules which together cover a range of contingencies, predictable and unpredictable.

Model included evolution through gene libraries, affinity maturation of the immune response and the clonal selection principle.

DiagnosisIshida (1993) Immune network model applied to the process diagnosis problemLater was elaborated as a sensor network that could diagnose sensor faults by evaluating reliability of data from sensors, and process faults by evaluating reliability of constraints among data.Main immune features employed:

Recognition is performed by distributed agents which dynamically interact with each other;Each agent reacts based solely on its own knowledge; andMemory is realized as stable equilibrium points of the dynamical network.

Comparing Approaches  AIS ANN EA

Components Attribute string in S Artificial neurons Strings representing chromosomes

Location of components Dynamic locations Pre-defined/dynamic (deterministic) locations

Dynamic locations

Structure Set of discrete or networked elements

Networked neurons Discrete elements

Knowledge storage Attribute strings/ network connections

Connection strengths Chromosomal strings

Dynamics Learning/evolution Learning Evolution

Metadynamics Elimination/recruitment of components

Constructive/pruning algorithms

Elimination/ recruitment of individuals

Interaction with other components

Through recognition of attribute strings or network connections

Through network connections Through recombination operators and/or fitness function

Interaction with the environment

Recognition of an input pattern or evaluation of an objective function

Input units receive the environmental stimuli

Evaluation of an objective function

Threshold Influences the affinity of elements

Influences neuron activation Influences genetic variations

Robustness Population/network of individuals

Network of individuals Population of individuals

State Concentration and affinity Activation level of output neurons

Genetic information in chromosomes

Control Immune principle, theory or process

Learning algorithm Evolutionary algorithm

Generalization capability

Cross-reaction Network extrapolation Detection of common schemas

Non-linearity Binding activation function Neuronal activation function Not explicit

Characterization Evolutionary and/or connectionist

According to the learning algorithm

Evolutionary

SummaryCovered much, but there is much work not covered (so apologies to anyone for missing theirs)ImmunologyImmune metaphors

Antibodies and their interactionsImmune learning and memorySelf/non-self

• Negative selection

Application of immune metaphors

The Future

Rapidly growing field that I think is very excitingMuch work is very diverse

Framework helps a littleMore formal approach required?

Wide possible application domainsWhat is it that makes the immune system unique?

More Information

http://www.cs.ukc.ac.uk/people/staff/jt6

http://www.msci.memphis.edu/~dasgupta/

http://www.dcs.kcl.ac.uk/staff/jungwon/

http://www.dca.fee.unicamp.br/~lnunes/

http://www.cs.unm.edu/~forrest/